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Record W2314905631 · doi:10.1061/9780784412121.226

Comparing Frequency and Time Domain Interpretations of Bender Element Shear Wave Velocities

2012· article· en· W2314905631 on OpenAlexafffund
Mark A. Styler, John A. Howie

Bibliographic record

VenueGeoCongress 2012 · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsTime domainFrequency domainShear (geology)WaveformSIGNAL (programming language)Interpretation (philosophy)GeologyAcousticsRange (aeronautics)Element (criminal law)MathematicsStatisticsComputer sciencePhysicsMathematical analysisEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Bender element testing is the most common means to measure the shear wave velocity across soil specimens in the laboratory. There are two classes of shear wave velocity interpretations: time domain and frequency domain, and different methods of interpretation tend to give different answers. Frequency domain approaches provide a means to account for the systematic effect of frequency on the interpreted velocity. Various proposed frequency domain methods have been published, but have so far obtained values that disagreed with more common time domain interpretations. This paper demonstrates that the errors in bender element interpretations of the shear wave velocity are predominantly systematic. The source of these systematic errors is traced to the frequency content of the selected trigger signal waveform and the interpretation method. The systematic errors are demonstrated using experimental data from bender element testing on a triaxial specimen of loose saturated Fraser River Sand. The results from a suite of bender element triggers and interpretation methods found that the shear wave velocity had a range of 30.9 m/s. It is demonstrated that this range is a systematic effect of the bender element trigger signal and interpretation method, not random error. The consequence of the systematic error is that there is no scientifically justifiable reason to take the average of a suite of trigger signals and interpretation methods. The average would not be an unbiased estimate of the mean velocity due to the unaccounted for effects of trigger signal type, frequency, and interpretation method.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.218
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2012
Admission routes2
Has abstractyes

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